Abstract:
A strong monsoon usually produces abundant crops,
although too much rainfall may produce devastating floods. Unlike
irrigated agriculture, rainfed farming is usually diverse and risk
prone. The India Meteorological Department (IMD) has long been
issuing seasonal forecasts of rainfall using statistical prediction
schemes that took firm root with the discovery of significant
correlation between the seasonal rainfall and various regional and
global climate phenomena. The prospect for monsoon forecast skill
based on dynamical models in which ocean conditions are perfectly
known, and are specified. Some of the monsoon predictors used is
linked to the El Niño-Southern oscillation (ENSO), along with
atmospheric and coupled models for additional input for better
results.
CFS (Climate forecast system) model simulated a weaker monsoon
circulation due to a cold bias at the surface over the Asian continent.
In India, the dynamical seasonal prediction (DSP) method,
introduced a few years ago, integrates the GCM (General
Circulation Model) with an ensemble of initial conditions for each
season in order to provide probabilistic seasonal forecasts.
Atmospheric and Coupled General Circulation Models (AGCM and
CGCM) are the main tools for dynamical seasonal prediction.
Dynamical Higher resolution using AGCMs with perfectly known
Ocean conditions specified, as well as coupled models are reported
to have insignificant forecast skill over ASM. The simulation of the
link between EQUINOO and the Indian monsoon rainfall could be
improved by investigations of the processes suggested to be
important, such as the modulation of the interplay between the local
Hadley circulation in the Indian longitudes and the Walker
circulation associated with the El Niño events. In addition, GOALS
(Global coupled ocean atmosphere-land model) or statistical based
models, such as: ANN, GA-ANN models etc. may be used to predict
the Indian Monsoon correctly.
